{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**提升（Boosting）一族算法的基本思想**：\n",
    "- 先从初始训练集训练一个基学习器\n",
    "- 再根据基学习器表现调整样本分布\n",
    "- 基于调整后的样本分布训练下一个学习器（使先前出错的样本得到更多关注）\n",
    "- 学习器达到预设数量后停止训练，将所有的学习的进行线性组合，作为最后的模型\n",
    "\n",
    "AdaBoost（Adaptive Boosting）是Boosting族中一个典型的**分类算法**。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AdaBoost算法解决二分类问题的流程\n",
    "输入：\n",
    "- 训练数据集$T={(x_1,y_1),(x_2,y_2),...,(x_N,y_N)}$，其中$x_i \\in \\chi \\subseteq R^n$，$y_i \\in \\gamma=\\{-1,+1\\}$\n",
    "- 弱学习算法\n",
    "\n",
    "输出：最终分类器$G(x)$\n",
    "\n",
    "\n",
    "**具体流程**\n",
    "1. 初始化训练数据的权值\n",
    "$$D_1=(\\omega_{11},...,\\omega_{1i},...,\\omega_{1N})，\\omega_{1i}=\\frac{1}{N}，i=1,2,...,N$$\n",
    "    -  初始时设置所有训练数据的权值相等\n",
    "2. for m= 1 to M：  # M是基分类器数量\n",
    "    1. 使用具有权值$D_m$的训练数据学习，获得到基分类器$$G_m(x)$$\n",
    "    2. 计算$G_m(x)$在训练集上的分类误差率$$e_m=\\sum_{i=1}^{N}P(G_m(x_i)≠y_i)=\\sum_{G_m(x_i)≠y_i}\\omega_{mi} \\qquad, \\omega_{mi}是第m论中第i个实例的权值$$\n",
    "        - 理解分类误差率：右边等号表示**分类器的分类误差率等于错分样本的权值之和（样本的权值分布随迭代可能变成非等概的）**。\n",
    "    3. 计算$G_m(x)$的系数$$\\alpha_m=\\frac{1}{2}In \\frac{1-e_m}{e_m}$$\n",
    "        - 定性理解基分类器的系数：由于分类误差率$e_m ≥0$。当$e_m≤\\frac{1}{2}$时，$\\alpha_m ≥ 0$，且$e_m$越小，$\\alpha_m$越大。**即误差率越小，基分类器的系数（影响力）就越大**。\n",
    "    4. 更新训练集权值分布$$D_{m_1}=(\\omega_{m+1,1},...,\\omega_{m+1,i},...,\\omega_{m+1,N})$$，其中$$\\omega_{m+1,i}=\\frac{\\omega_{mi}}{Z_m}e^{(-\\alpha_m y_iG_m(x_i))}, \\qquad i=1,2,...,N$$或$$\\omega_{m+1,i}=\\left\\{\\begin{matrix} \n",
    "  \\frac{\\omega_{mi}}{Z_m}e^{-\\alpha_m}, G_m(x_i)=y_i \\\\\n",
    "  \\frac{\\omega_{mi}}{Z_m}e^{\\alpha_m} , G_m(x_i)\\neq y_i\n",
    "\\end{matrix}\\right.$$ $Z_m$是规范化因子$$Z_m=\\sum_{i=1}^{N}\\omega_{mi}e^{(-\\alpha_m y_i G_m(x_i))}$$\n",
    "        - **训练数据集权值分布的调整策略**是：提高上个基分类器中错分类样本的权值\n",
    "3. 构建基分类器的线性组合$$f(x)=\\sum_{m=1}^{M} \\alpha_m G_m(x)$$，得到最终分类器\n",
    "$$G(x)=sign(f(x))$$\n",
    "\n",
    "\n",
    "**遗留问题**：\n",
    "1. 如何确定基分类$G_m$以及其系数$\\alpha_m$？\n",
    "    - 这个问题的推导见下面“损失函数的优化”一节\n",
    "2. 如何理解权值更新步骤的公式？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AdaBoost解决回归问题的流程\n",
    "AdaBoost回归算法变种很多，下面的算法为Adaboost R2回归算法过程。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "输入：\n",
    "- 训练数据集$T={(x_1,y_1),(x_2,y_2),...,(x_N,y_N)}$，其中$x_i \\in \\chi \\subseteq R^n$，$y_i \\in \\gamma=\\{-1,+1\\}$\n",
    "- 弱学习算法\n",
    "\n",
    "输出：强学习器$f(x)$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**具体流程**\n",
    "1. 初始化训练数据的权值\n",
    "$$D_1=(\\omega_{11},...,\\omega_{1i},...,\\omega_{1N})，\\omega_{1i}=\\frac{1}{N}，i=1,2,...,N$$\n",
    "    -  初始时设置所有训练数据的权值相等\n",
    "2. for m= 1 to M：  # M是基学习器数量\n",
    "    1. 使用具有权值$D_m$的训练数据学习，获得到基学习器$$G_m(x)$$\n",
    "    2. 计算$G_m(x)$在训练集上的最大误差$$E_m=max|y_i-G_m(x_i)|,i=1,2,...,N$$\n",
    "    3. 计算每个样本的相对误差（有以下几种不同的计算方式）\n",
    "        - 定义为线性误差时：$$e_{mi}=\\frac{|y_i-G_m(x_i)|}{E_m}$$\n",
    "        - 定义为平方误差时：$$e_{mi}=\\frac{(y_i-G_m(x_i))^2}{E_m^2}$$\n",
    "        - 定义为指数误差时：$$e_{mi}=1-e^{\\frac{-|y_i-G_m(x_i)|}{E_m}}$$\n",
    "    4. 计算回归误差率$$e_m=\\sum_{i=1}^N \\omega_{mi}e_{mi}$$\n",
    "    5. 计算弱学习器的系数$$\\omega_{m+1,i}=\\frac{\\omega_{mi}}{Z_m}\\alpha_m^{1-e_{mi}}$$，其中$Z_m$是规范化因子$$Z_m=\\sum_{i=1}^N \\omega_{mi}\\alpha_m^{1-e_mi}$$\n",
    "3. 采用加权平均法最终得到强学习器$$f(x)=\\sum_{m=1}^{M}(In\\frac{1}{\\alpha_m})G_m(x)$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AdaBoost算法的正则化\n",
    "为防止AdaBoost过拟合，可以加入**正则化项（也称作步长）$\\nu$**。对于迭代过程$$f_m(x)=f_{m-1}(x)+\\alpha_m G_m(x)$$，加上正则化项后变成$$f_m(x)=f_{m-1}(x)+\\nu\\alpha_m G_m(x)$$，其中$0<\\nu≤1$。\n",
    "\n",
    "- 当$\\nu$较小时，迭代次数M会增大，表示需要更多的弱学习器。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 前向分布算法\n",
    "AdaBoost算法是前向分布加法算法的特例。可以证明，当损失函数使用指数函数时，前向分布算法可以推出AdaBoost算法（见《统计学习方法 第二版》P164）。\n",
    "## 加法模型\n",
    "即目标函数为\n",
    "$$f(x)=\\sum_{m=1}^M \\beta_m b(x;\\gamma_m)$$\n",
    "其中$\\beta_m$是每个基函数的系数，$\\gamma_m$是每个基函数的参数，$b(x;\\gamma_m)$就是一个基函数。可见，基分类器的线性组合可以描述为基函数的线性组合。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 前向分布算法\n",
    "给定训练数据和损失函数$L(y,f(x))$时，加法模型的损失函数最小化问题是\n",
    "\n",
    "<img src=\"AdaBoost优化目标.png\" hight=\"30%\" width=\"30%\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上述问题难以直接优化，因此采用**前向分布算法的思想：从前向后逐个训练分类器，每增加一个分类器都使得损失函数更小，逐步逼近最优解。**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**前向分布算法的具体步骤**：\n",
    "1. 初始化$f_0(x)=0$\n",
    "2. for m = 1 to M：\n",
    "    - 最小化损失函数得到第m个基分类器的系数和参数\n",
    "    \n",
    "    <img src=\"前向分布算法最小化损失.png\" hight=\"50%\" width=\"50%\">\n",
    "    - 得到第m次迭代后的分类器\n",
    "    <img src=\"更新函数.png\" hight=\"50%\" width=\"50%\">\n",
    "3. 得到加法模型$f(x)=f_m(x)=\\sum_{m=1}^M \\beta_m b(x;\\gamma_m)$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 损失函数的优化\n",
    "AdaBoost分类算法使用的是指数损失函数，其形式如下\n",
    "$$L(f(x),y)=\\sum_{i=1}^N e^{-y_i f(x_i)}$$，其中$y_i$是标签值，$f(x_i)$是预测值。\n",
    "\n",
    "那么，**如何优化损失函数，即确定基分类器$G_m$以及系数$\\alpha_m$呢**？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**首先，这个优化问题可以描述为**$$(\\alpha_m,G_m(x))=arg \\ min_{\\alpha,G}\\sum_{i=1}^N e^{-y_i f_m(x_i)}$$。\n",
    "由于$f_m(x_i)=f_{m-1}(x_i)+\\alpha G(x_i)$，所以$$(\\alpha_m,G_m(x))=arg \\ min_{\\alpha,G}\\sum_{i=1}^N e^{-y_i f_{m-1}(x_i)-y_i\\alpha G(x_i)}$$，如果令$\\bar{\\omega}_{mi}=e^{-y_if_{m-1}(x_i)}$，可见$\\bar{\\omega}_{mi}$不依赖$\\alpha$和$G$，与最小化损失函数无关。但$\\bar{\\omega}_{mi}$依赖$f_{m-1}(x)$，这个值随每次迭代而变化。故$$(\\alpha_m,G_m(x))=arg \\ min_{\\alpha,G}\\sum_{i=1}^N \\bar{\\omega}_{mi}e^{-y_i \\alpha G(x_i)}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**然后**，这个优化问题可以分两步解决（假设$\\alpha_m^*$和$G_m^*(x)$是AdaBoost算法得到的$\\alpha_m$和$G_m(x)$）：\n",
    "1. 求$G_m^*(x)$\n",
    "    - 因为$y_iG(x_i)=1（预测正确） 或 -1（预测错误）$，所以$$\\sum_{i=1}^N \\bar{\\omega}_{mi}e^{-y_i \\alpha G(x_i)}=\\sum_{y_i≠G(x_i)}\\bar{\\omega}_{mi}e^\\alpha+\\sum_{y_i=G(x_i)}\\bar{\\omega}_{ki}e^{-\\alpha}$$ $$=(e^\\alpha-e^{-\\alpha})\\sum_{i=1}^N \\bar{\\omega}_{mi}I(y_i≠G(x_i))+e^{-\\alpha}\\sum_{i=1}^N \\bar{\\omega}_{mi}$$\n",
    "    - 从上式可以得知$$G_m^*(x)=arg \\ min_G \\sum_{i=1}^N \\bar{\\omega}_{mi}I(y_i≠G(x_i))$$\n",
    "2. 求$\\alpha_m^*$\n",
    "    - 将$G_m^*(x)$代入到损失函数，然后对$\\alpha$求导，令其为0，可得$$\\alpha_m^*=\\frac{1}{2}In \\frac{1-e_m}{e_m}$$，$e_m$是分类误差率。\n",
    "\n",
    "求得系数和基学习器后就可以得出下一轮迭代的训练集权值分布。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 实战\n",
    "sklearn中的AdaBoost类库是AdaBoostClassifier（基于SAMME或SAMME.R）与AdaBoostRegressor（基于Adaboost.R2），前者用于分类，后者用于回归。\n",
    "\n",
    "调参时，主要分为两部分：\n",
    "1. AdaBoost框架相关\n",
    "2. 基分类器相关"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AdaBoostClassifier和AdaBoostRegressor的框架参数\n",
    "1. base_estimator：表示要使用的基学习器，这些基学习器需要支持权重和一些方法。默认是CART分类树（DecisionTreeClassifier），一般可以是决策树或神经网络。\n",
    "    - 注：该属性支持AdaBoostClassifier和AdaBoostRegressor\n",
    "2. algorithm：{'SAMME', 'SAMME.R'}, 默认是'SAMME.R'。表示具体实现的算法。两种算法的区别在于基学习器的权重度量。SAMME.R使用了对样本集分类的预测概率大小来作为基学习器权重，迭代较\\`SAMME\\`更快。\n",
    "    - 注：\n",
    "        - 该属性只支持AdaBoostClassifier\n",
    "        - 当该属性是\\`SAMME.R\\`时，基学习器需要支持概率预测。\n",
    "3. loss：{'linear', 'square', 'exponential'}, 默认是'linear'。这个参数就是AdaBoost回归算法中的样本相对误差。\n",
    "    - 注：该属性只支持AdaBoostRegressor\n",
    "4. n_estimators：int, default=50。表示迭代次数（最大的基学习器数）。**该值越大，容易过拟合；越小，容易欠拟合**。\n",
    "    - 注：该属性支持AdaBoostClassifier和AdaBoostRegressor\n",
    "5. learning_rate：float, 默认是1。这个就是正则化系数$\\nu$，可以调整基学习器的贡献。\n",
    "    - 注：该属性支持AdaBoostClassifier和AdaBoostRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AdaBoostClassifier和AdaBoostRegressor的基学习器参数\n",
    "默认的基学习参数是CART分类树DecisionTreeClassifier和CART回归树DecisionTreeRegressor。\n",
    "\n",
    "这部分的参数在CART决策树有介绍。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AdaBoostClassifier实战"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "导入相关模块："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.datasets import make_gaussian_quantiles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "生成正态分布的随机数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 生成2维正态分布，生成的数据按分位数分为两类，500个样本,2个样本特征，协方差系数为2\n",
    "X1, y1 = make_gaussian_quantiles(cov=2.0, n_samples=500, n_features=2, n_classes=2, random_state=1)\n",
    "# 生成2维正态分布，生成的数据按分位数分为两类，400个样本,2个样本特征均值都为3，协方差系数为2\n",
    "X2, y2 = make_gaussian_quantiles(mean=(3, 3), cov=1.5,n_samples=400, n_features=2, n_classes=2, random_state=1)\n",
    "#讲两组数据合成一组数据\n",
    "X = np.concatenate((X1, X2))\n",
    "y = np.concatenate((y1, - y2 + 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可视化数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x20646893488>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:, 0], X[:, 1], marker='o', c=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在使用AdaBoostClassifier进行拟合，先进行建立模型、训练数据（200次迭代、学习率为0.8）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostClassifier(algorithm='SAMME',\n",
       "                   base_estimator=DecisionTreeClassifier(max_depth=2,\n",
       "                                                         min_samples_leaf=5,\n",
       "                                                         min_samples_split=20),\n",
       "                   learning_rate=0.8, n_estimators=200)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bdt = AdaBoostClassifier(DecisionTreeClassifier(max_depth=2, min_samples_split=20, min_samples_leaf=5),\n",
    "                         algorithm=\"SAMME\",\n",
    "                         n_estimators=200, \n",
    "                         learning_rate=0.8)\n",
    "bdt.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "绘制拟合区域："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),\n",
    "                     np.arange(y_min, y_max, 0.02))\n",
    "\n",
    "Z = bdt.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "Z = Z.reshape(xx.shape)\n",
    "cs = plt.contourf(xx, yy, Z, cmap=plt.cm.Paired)\n",
    "plt.scatter(X[:, 0], X[:, 1], marker='o', c=y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "输出预测的精准率："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.9133333333333333\n"
     ]
    }
   ],
   "source": [
    "print(\"Score:\", bdt.score(X,y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "此时增加迭代次数到300，再次训练，再次预测的精准率："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.9622222222222222\n"
     ]
    }
   ],
   "source": [
    "bdt = AdaBoostClassifier(DecisionTreeClassifier(max_depth=2, min_samples_split=20, min_samples_leaf=5),\n",
    "                         algorithm=\"SAMME\",\n",
    "                         n_estimators=300,   # \n",
    "                         learning_rate=0.8)\n",
    "bdt.fit(X, y)\n",
    "print(\"Score:\", bdt.score(X,y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可见迭代次数增加时，集成学习器拟合效果会变好。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "保持迭代次数为300不变，再将学习率从0.8减小到0.5，再观察预测的精准率："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.8944444444444445\n"
     ]
    }
   ],
   "source": [
    "bdt = AdaBoostClassifier(DecisionTreeClassifier(max_depth=2, min_samples_split=20, min_samples_leaf=5),\n",
    "                         algorithm=\"SAMME\",\n",
    "                         n_estimators=300, learning_rate=0.5)\n",
    "bdt.fit(X, y)\n",
    "print(\"Score:\", bdt.score(X,y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可见基分类器不变时，减小学习率，拟合效果会下降。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AdaBoostRegressor实战"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先随机生成100个样本。\n",
    "\n",
    "分别建立CART回归树（深度为4）和AdaBoostRegressor模型（基学习器为深度为4的CART回归树，迭代次数为300），训练后将预测结果进行对比。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Automatically created module for IPython interactive environment\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEWCAYAAAB42tAoAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy86wFpkAAAACXBIWXMAAAsTAAALEwEAmpwYAABq/ElEQVR4nO2deXxU1fXAvyf7xr5vmSAgu4RNtLghqAhabd0bF6yKWqxtf9WKYhWX1KVW69ZaXNHEfbdqVRRRBBVQdpA1CWEPe0gCWc7vj/smTpKZZJLMlnC/n8/7zMx999173ryZd96959xzRFWxWCwWi8UXUeEWwGKxWCyRjVUUFovFYqkVqygsFovFUitWUVgsFoulVqyisFgsFkutWEVhsVgsllqxisIScYjIJBGZG6a+C0XkqDrqnCgiP4VKJotvROQ2EXkm3HI0d6yiaOaISI6IFDs3wD0i8qGI9Ahyn1+KyNVBajtNRNQ5n0IR2S4i/xWR0wLRvqqmqOqGOup8rap9A9GfG0f5uM/pYLVzLBSR1ED259Fv9e8zR0SmBqOvYKCqf1PVoPzWLD9jFcWRwdmqmgJ0AbYDj4dZnkDQ2jmnIcBnwDsiMim8IjUcR/mkOOc00Clu7S5T1Tx3XRGJCYII7u/zfOCvgVK8ngRJbksIsIriCEJVS4A3gQHuMhFpJSIvishOEckVkdtFJMrZF+V8zhWRHU69Vs6+BBHJEpFdIrJXRBaISCcRyQROBJ5wnlCfcOr3E5HPRGS3iPwkIhd6yNBORN4Xkf0i8j3Qqx7ntE1VHwWmAw94yN5VRN5yzmujiNzo0V+0M2WxXkQOiMgi9yjLebru7byfICIrnTqbReQmp/wUEcn3aK+/M4raKyIrROSXHvteEJEnnZHcARH5TkT8Pj+njeki8qbzfe8HJjnX7VkR2erIdq+IRHsc81sRWeWMIj8REZef3+dCYAWQ7k9bInK6cz33ici/RGSOezQpZgrxGxF5RER2A9NFJF5EHhKRPGc0+JSIJDr12zujw73O7+Rrj+t5i3OeB5z+xnp8N1ke8vzSuQZ7nWvS32NfjojcJCJLHXlfE5GE+lyLIxZVtVsz3oAcYJzzPgmYCbzosf9F4D2gBZAGrAGucvb9FlgHHAWkAG8DLzn7rgU+cNqMBoYDLZ19XwJXe/SRDGwCrgRigGFAATDQ2f8q8LpTbxCwGZjr43zSAAViqpUf5ZT3xzwALQLuAOKcfRuAM5y6NwPLgL6AYEYl7Zx9CvR23m8FTnTetwGGOe9PAfKd97HOd3Sb09epwAGgr7P/BWA3cKxz7tnAq3VcsyrniFGCpcC5zrklAu8C/3G+s47A98C1Tv1zHZn6O33eDszzs6/jgCLgV3W1BbQH9gO/dvb9wZHzamf/JKAM+L2zPxH4J/A+0Bbzm/sAuM+pfx/wlPOdxmIeOMS5TpuArh4y9/L4brKc90cDB4HTnOP/4sge5/Ff+B7o6vS/Crgu3P/RprCFXQC7BfkCmz9HIbDX+dNuAQY7+6KBQ8AAj/rXAl867z8Hfuexr69zI4jBKJF5wDFe+vySqoriIuDranX+A9zpyFAK9PPY9zfqrygSnPLRwCggr9r+W4Hnnfc/Aef4aN9TUeQ530fLanVO4WdFcSKwDYjy2P8KMN15/wLwjMe+CcDqOq5ZlXN0boZfeezv5Fy3RI+yS4DZzvuPcZS98zkKc/N31dLXXqDYef8QIHW1BVwOzPfYJ5gbuqeiyKu2/yDOTd4pOx7Y6Ly/G/PQ0ruajL2BHcA4ILbavun8rCj+CrxeTdbNwCke/4VLPfY/CDwV7v9oU9js1NORwbmq2hqIB24A5ohIZ8wTYRyQ61E3F+jmvO/qZV8M5kb1EvAJ8KqIbBGRB0Uk1kf/LmCUMx2wV0T2AhlAZ6CD0+amav3UF7fMu53+ulbr7zZHboAewHo/2jwPc2PPdaZUjvdSpyuwSVUrqsnfzePzNo/3RZjRWX3x/H5cmCfmrR7n9x/MyMK9/1GPfbsxN2lPmarT3pHrJowidF/L2trq6imXmrtvPlXxlLsDZgS6yKO9/znlAH/HjAA+FZEN4hjVVXUd8EeMUtghIq+KSFcv51Dl9+pck00E/loccVhFcQShquWq+jZQDpyAmf4pxdwM3KRinsLAjD6q7ysDtqtqqarepaoDgF8AZ2GeMME8lXqyCZijqq09thRVvR7Y6bTp6YnVEA+fX2GeOn9y+ttYrb8WqjrBQ5467QSqukBVz8HcgN/FTI9VZwvQwz2X7iH/Zi91G4Pnd7oJM6Jo73F+LVV1oMf+a6udf6Kqzqu1A/P7+AdQAvzOj7a2At3dx4uIeH72IncBZtQy0KOtVmqM6KjqAVX9s6oeBZwN/J/bFqGqL6vqCZjfowIPeDmFKr9XR54eBP5aHHFYRXEEIYZzMPPtq1S1HHPzyxSRFo6R8v8At3HwFeBPItJTRFIwU0KvqWqZiIwRkcGOAXU/RuGUO8dtx9gF3PwXOFpELhORWGcbKSL9HRnexhg6k0RkAHBFPc6pk4jcgJnGutV5ivwe2O8YQBPFGK8HichI57BngHtEpI/znRwjIu2qtRsnIhki0kpVS51zLKcm32GmU/7inNcpmJvcq/6eQ31R1a3Ap8A/RKSlGKeDXiJyslPlKeBWERnonEsrEbmgHl3cjzmfhDra+hAYLCLnivFomoIZJfqSuwJ4GnhERDo67XUTkTOc92eJSG/nBu/+vstFpK+InCoi8RglVoz3a/E6MFFExjqj2z9jFGqtCtJSN1ZRHBl8ICKFmD9fJnCFqq5w9v0ec6PbAMwFXgaec/Y9h5li+grYiPmT/t7Z1xnjQbUfYxScw88K5lHgfDFeMo+p6gHgdOBizFPfNswTYbxT/wbMFMA2zJz+836c014ROYgxSk8ALlDV58A8GWNu1umO3AUY5dDKOfZhzE3lU0f+ZzGG1upcBuSI8TS6Dri0egVVPQz8EjjT6edfwOWqutqPc2gMl2OmDVcCezDXoosj0zuY7/dVR/bljnz+8qHT5jW1taWqBcAFmLn+XRhvuoWYm7MvbsFML33rtDcLY/sC6ON8LgTmA/9S1S8xv5P7Md/vNswI77bqDavqT5hr9LhT92yMa/jhepy7xQtug5XFYrE0Cmf6LR/IUNXZ4ZbHEjjsiMJisTQYETlDRFo700K3YQzd34ZZLEuAsYrCYrE0huMxHmTuqZ5zVbU4vCJZAo2derJYLBZLrdgRhcVisVhqpVkG6Wrfvr2mpaWFWwyLxWJpMixatKhAVTt429csFUVaWhoLFy4MtxgWi8XSZBARnxER7NSTxWKxWGrFKgqLxWKx1IpVFBaLxWKplWZpo7BYLHVTWlpKfn4+JSUl4RbFEkISEhLo3r07sbG+gj3XxCoKi+UIJT8/nxYtWpCWloaJw2dp7qgqu3btIj8/n549e/p9nJ16sgCQnZ1NWloaUVFRtG/fnvbt2xMVFUVaWhrZ2dnhFs8SBEpKSmjXrp1VEkcQIkK7du3qPYq0IwoL2dnZTJ48maKiIgB27dpVuS83N5fJkycDkJGRERb5LMHDKokjj4ZcczuisDBt2rRKJeGNoqIipk2bFkKJLBZLJBE2RSEiPURktoisEpEVIvIHL3VERB4TkXUislREhoVD1uZOXl5eQOpYLJbmSThHFGXAn1W1P3AcMMXJbubJmZhkJn2AycC/QyvikUFqat2ZR/2pY7FECosXL+ajjz6q/Pz+++9z//33B6Ttf/7zn7WOwAPN+PHjad26NWeddVbI+qxO2BSFqm5V1R+c9wcwWdKqJ38/B3hRDd8CrUWkS4hFbfZkZmaSlJTkc39SUhKZmZkhlMhiaRzVFcUvf/lLpk6dGpC2G6Ioysu9ZW71j5tvvpmXXnqpwccHgogwZotIGjAUk3/Yk26Y5O5u8p2yrV7amIwZddin33riNlJPmzaNvLw82rZtC8Du3btJTU0lMzPTGrKbOXJXcIzaemftaQxycnI488wzOeGEE5g3bx7dunXjvffeIzGxZmba9evXM2XKFHbu3ElSUhJPP/00/fr144033uCuu+4iOjqaVq1aMWvWLO644w6Ki4uZO3cut956K8XFxSxcuJAnnniCSZMmkZiYyOrVq8nNzeX5559n5syZzJ8/n1GjRvHCCy8AcP3117NgwQKKi4s5//zzueuuu3jsscfYsmULY8aMoX379syePZtXXnmFv/3tb6gqEydO5IEHHgAgJSWF//u//+OTTz7hH//4B//97395//33iYmJ4fTTT+ehhx7y6zscO3YsX375Zb2+90ATdmO2iKQAbwF/VNX91Xd7OcTrL09VZ6jqCFUd0aGD1wCIllrIyMggJyeHiooKCgoKKCgooKKigpycHIBK11nrLmsJNGvXrmXKlCmsWLGC1q1b89Zbb3mtN3nyZB5//HEWLVrEQw89xO9+9zsA7r77bj755BOWLFnC+++/T1xcHHfffTcXXXQRixcv5qKLLqrR1p49e/jiiy945JFHOPvss/nTn/7EihUrWLZsGYsXLwbMSHvhwoUsXbqUOXPmsHTpUm688Ua6du3K7NmzmT17Nlu2bOGWW27hiy++YPHixSxYsIB3330XgIMHDzJo0CC+++47BgwYwDvvvMOKFStYunQpt99+O2A8DtPT02ts559/fuC/6EYQ1hGFiMRilES2qr7tpUo+0MPjc3dgSyhksxiqu85ad9nmSV1P/sGkZ8+epKenAzB8+PDKhxNPCgsLmTdvHhdccEFl2aFDhwAYPXo0kyZN4sILL+TXv/61X32effbZiAiDBw+mU6dODB48GICBAweSk5NDeno6r7/+OjNmzKCsrIytW7eycuVKjjnmmCrtLFiwgFNOOQX3w2lGRgZfffUV5557LtHR0Zx33nkAtGzZkoSEBK6++momTpxYaW/IyMhoEv+jsCkKMc68zwKrVPVhH9XeB24QkVeBUcA+Va0x7WQJHt5cZ93usk3hB26JfOLj4yvfR0dHU1xcM5NqRUUFrVu3rnza9+Spp57iu+++48MPPyQ9Pd1rHV99RkVFVek/KiqKsrIyNm7cyEMPPcSCBQto06YNkyZN8rpIrbYMoQkJCURHRwMQExPD999/z+eff86rr77KE088wRdffEF2djZ///vfaxzbu3dv3nzzzTrPI1SEc+ppNHAZcKqILHa2CSJynYhc59T5CNgArAOeBn4XJlmPWHy5xTYFd1nP1eZ2yqxp07JlS3r27Mkbb7wBmBv0kiVLAGO7GDVqFHfffTft27dn06ZNtGjRggMHDjS4v/3795OcnEyrVq3Yvn07H3/8ceU+z7ZHjRrFnDlzKCgooLy8nFdeeYWTTz65RnuFhYXs27ePCRMm8M9//rNSmWVkZLB48eIaWyQpCQjjiEJV5+LdBuFZR4EpoZHI4o3U1FRyc2vmM4l0hwE7Zdb8yM7O5vrrr+fee++ltLSUiy++mCFDhnDzzTezdu1aVJWxY8cyZMgQUlNTuf/++0lPT+fWW2+td19Dhgxh6NChDBw4kKOOOorRo0dX7ps8eTJnnnkmXbp0Yfbs2dx3332MGTMGVWXChAmcc845Ndo7cOAA55xzDiUlJagqjzzyiN+ynHjiiaxevZrCwkK6d+/Os88+yxlnnFHvc2oMUtvQqakyYsQItRnuAkP1Gy4Yd9kZM2ZE9A03LS3Nq4JzuVxe58CPRFatWkX//v3DLYYlDHi79iKySFVHeKsfdq8nS2STkZHBjBkzcLlciAgulyvilQQ07SkziyXSsIrCUieerrM5OTkRryTA99SYqlp7RYQzZcqUGu6izz//fLjFOqKJiAV3FkugyczMrDFl5sbaKyKbJ598MtwiWKphRxRHOm++CcOHQ79+4HJBly7et6OOgjlzmownkeeUmTdsRFyLxX+sMftI59hjYcECv6puGjaMfqtXNznDdlRUlFd/dxGhoqIiDBJFBtaYfeRSX2O2nXo6Qnjsu8d4fnG1eV5Vvlm6jCTg0lv6sLtFDGUxUWg1p+VWhWW8fu9PdFjyA9GXY8ahnwKbmsbiu6bq4muxRApWURwh/GP+P8jbV9XjJ3UvJB2C7cmQnbjWBH4v83JwNMztASflwdn74OVjgCFUhmuMdE8ib/YKGxHXYvEfa6M4Qjh4+CAAn1/+OT9M/oF7e9xL+suxAKwsBZ6ChBcSuLfHvfww+YcaW89r/gLABV86DXoE94z0J/Om6uJraTjNJR9Fbm4uw4cPJz09nYEDB/LUU09V7lu0aBGDBw+md+/e3HjjjZXTq7t37+a0006jT58+nHbaaezZs6fxgqhqs9uGDx+ulqokZSYp09HCQ4WqqupyufTPoAr6uInIq4C6XC7vDWzerBUiWixoi6kol5v6SUlJmpWVFboTsQSMlStXhluEoPH888/rlClTgtK2y+XSnTt31uuYsrKyBvV16NAhLSkpUVXVAwcOqMvl0s2bN6uq6siRI3XevHlaUVGh48eP148++khVVW+++Wa97777VFX1vvvu07/85S812vV27YGF6uOeakcURwCqSlGpeQJKjDVDgby8PNzpBFd61PU5jdS1K3LCCSQonLUGSMQ+mTcnRIKz1UFOTg79+/fnmmuuYeDAgZx++ulegwKCiek0fvx4hg8fXhnWAuCNN95g0KBBDBkyhJNOOonDhw9zxx138Nprr5Gens5rr73GCy+8wA033ADApEmTuP766xkzZgxHHXUUc+bM4be//S39+/dn0qRJlf1df/31jBgxgoEDB3LnnXcCVMlHMWbMGABeeeUVBg8ezKBBg7jlllsqj09JSeGOO+5g1KhRzJ8/n6lTpzJgwACOOeYYbrrpJr8uS1xcXGXQwkOHDlU6X2zdupX9+/dz/PHHIyJcfvnlleHN33vvPa644goArrjiisryRuFLgzTlzY4oqlJcWqxMR+Pvia8sc7lc+q0zojjJnxGFqupjj6mCvt0PTX0kNfiCN4CsrCx1uVwqIupyuexopxaqPFU6v4WAb3WwceNGjY6O1h9//FFVVS+44AJ96aWXvNY99dRTdc2aNaqq+u233+qYMWNUVXXQoEGan5+vqqp79uxR1ZojCs/PV1xxhV500UVaUVGh7777rrZo0UKXLl2q5eXlOmzYsEpZdu3apapmNHDyySfrkiVLVLXqiGLz5s3ao0cP3bFjh5aWluqYMWP0nXfecb5S9LXXXqts6+ijj9aKiooqcmZlZemQIUNqbOedd16l7Hl5eTp48GBNTEzUJ554QlVVFyxYoGPHjq2s89VXX+nEiRNVVbVVq1ZVvrfWrVvX+C7tiMJSg+qjCYDMe++tHFGscF7rNPCedx4qwplroXTv7uAI2wjccalyc3NR1cqFdZG61iOiCJaq8IP65qNIT0/n2muvZetWk3HAnY/i6aef9jvlqLd8FFFRUZX5KABef/11hg0bxtChQ1mxYgUrV66s0Y5nPoqYmJjKfBSAz3wUb7/9dmXqYX+ix/bo0YOlS5eybt06Zs6cyfbt21Ev3634MYJrKFZRHAEUl5qhfFJsUuWCudsuu4wWwE4Rdvtr4O3aFUaPJqEcTl5eSGl5aWhOwE+q585oA9xRVETCtdfC5ZfDlVfCggVNZtHgkUL1fBRlZTVd7zzzUbi3VatWASYfxb333sumTZtIT09n165dfvdZVz6Kzz//nKVLlzJx4sSA5KM477zzePfddxk/fjxQvwx3Xbt2ZeDAgXz99dd0796d/Pz8yn35+fl07doVgE6dOlUq0a1bt9KxY8c6v4+6sIriCMA9oigrKat84nYvtVkpwksvveR3DCf51a8AGLsB9pQEwJsigFS3r7wE3AKcd/AgvPQSvPACO3/zGzvqaIIcqfko8vPzK202e/bs4ZtvvqFv37506dKFFi1a8O2336KqvPjii5XhzX/5y18yc+ZMAGbOnOk17Hl9CauiEJHnRGSHiCz3sf8UEdnnkdjojlDL2BwoLnN+aNv3VD5xD3T2La+oqF8oCycVZJ/dsLs4sqafPN10bwAmAruBm9u1g+efh/h42q1bR5KPjH2WyCY7O5tnn32WIUOGMHDgQN577z0Abr755kpj8kknncSQIUMYM2YMK1eurDRm1xfPfBS//e1vveajGDNmDF26dKnMRzFkyBCGDRvmMx/FWWedxTHHHMPJJ5/sdz6KVatWMWrUKIYMGcLJJ5/MTTfdVJm29d///jdXX301vXv3plevXpx55pkATJ06lc8++4w+ffrw2WefMXXq1Hqffw18GS9CsQEnAcOA5T72nwL8t77tWmN2VeZvmq9MR7n6Z6P1M84s8vWgIuJ/Yzk5qqBbUtBv8r4JntANICsrS5OSknQQaLFzfpfExf1s0B4/XhX0cg/jvXur13fQTGjO7rGW2mlSxmxV/Qrz0GcJIm4bRXz0z3Ox7hHFSuq5YK5HDw7HRtGlEPbtzK+7fgjJyMjg2See4M3YWBKAV1JSmPjccz9PqTkJ7c/ycmykLxq0WMJJU7BRHC8iS0TkYxEZ6KuSiEwWkYUisnDnzp2hlC/icdso+vXqV+lt4fZ42piYWL9QFlFRbO/cAoCKNT8FUsyAcHFREX1LS+Hoo7lk27aqdpeJEwE4A4j1OMaG84gsbD6KyCPSYz39ALhUtVBEJgDvAn28VVTVGcAMMNFjQyZhE8Bto+jt6s3NM27miVtuoeXmzeyKiuJvTz9d7wVzu7u3o8emfci6dcEQt3HMm2de//xnSE6uui8tDQYNouXy5ZzfsSOv7txJamoqmZmZdtFgBGHzUUQeET2iUNX9qlrovP8IiBWR9mEWq8nhuY4iIyOD+c8+C0C7E09s0A2yMLUzAPEbIjAY4KJF5nX4cO/7nemnl3/zmyaVsc9iCScRrShEpLM4q0hE5FiMvHU7SVuq4FYUSTFm2okVzhK7AQN8HFE7JUeZ+fwWuVsbLVtAOXAA1qyB2FgYNMh7HUdR8MEHfi8Is1iOdMI69SQir2A8m9qLSD5wJ870sao+BZwPXC8iZUAxcLFjnbfUA88FdwC4V5gO9GnyqZWyXkcB0Ca/oNGyBZQffzQ3/8GDwWMRVRWOOw7atoX1641S6ds3tDJaLE2QcHs9XaKqXVQ1VlW7q+qzqvqUoyRQ1SdUdaCqDlHV41R1Xjjl9YdIXPVbJYRHWRl8/bXZ0UBFEd23HwCdtuwPiHwB44cfzOuwYb7rREfDhAnm/X//G3yZLD7Zu3cv//rXvxp07IQJE9i7d2+tde644w5mzZrVoPbDyaRJk6qE8IgEInrqKZLxphAiNdaQ25idFJsEzzxjnqTT0uD44xvUXnJqLw7EQcvCUtgdQd7Nddkn3Linn6yiCCu1KYq6YjZ99NFHtG7dutY6d999N+PGjWuoeBYPrKJoAL4Uwh/+8IcaCU0iYdWve0TRuhj4619N4d//7nt6pg7aJrVjXVvnw9q1jRcwUPirKE4/HaKiYO5c2B9ho6IIJtCj5alTp7J+/XrS09O5+eab+fLLLxkzZgy/+c1vKlcfn3vuuQwfPpyBAwcyY8aMymPT0tIoKCioNUy555N5Wload955J8OGDWPw4MGVIcp37tzJaaedxrBhw7j22mtxuVwUFFSdUi0vL2fSpEkMGjSIwYMHV66qfvrppxk5ciRDhgzhvPPOq/zv+xvGPCUlhT//+c8MGzaMsWPH4s2tf9GiRZx88skMHz6cM844ozKG02OPPVYZsvziiy9u1HXwC18r8ZryFuyV2S6Xq8bK3tq2cK/6ve6D65Tp6I+/GWtiep50kqoT7rgh7Cjcoa8OdOKDvvhiACVtBAcOqIqoxsSoFhfXXf8XvzDyv/128GWLUOqzMtu96t3zd93YpFUbN27UgQMHVn6ePXu2JiUl6YYNGyrL3KG+i4qKdODAgVpQUKCqP4f6ri1M+RVXXKFvvPFGZf3HHntMVVWffPJJveqqq1RVdcqUKfq3v/1NVVU//vhjBWokJVq4cKGOGzeu8rM7RLhbFlXVadOmVbbvbxhzoPL7u+uuu6qEQX/jjTf08OHDevzxx+uOHTtUVfXVV1/VK6+8UlVVu3TpUpnQyC1PfWhSK7ObKvXNER3uVb9FZUX0KYBjXv/SJJP55z/9SirjizaJbVjrjCge/f0NkWGPWbLEGLIHDoSEhLrrO3Fx8Aj2ZvFN9ci8EJzR8rHHHkvPnj0rPz/22GMMGTKE4447jk2bNrHWywjWnzDlAL/+9a9r1Jk7d27lE/n48eNp06ZNjeOOOuooNmzYwO9//3v+97//0bJlSwCWL1/OiSeeyODBg8nOzmaF25sQ/8KYR0VFcdFFFwFw6aWXMnfu3Cr9/vTTTyxfvpzTTjuN9PR07r333sqIsccccwwZGRlkZWURExN8nySrKBqArxt/u3btKlc+u4mEVb/FpcX87XOIKiuHq66CoUMb1V5MVAw57c3a5k4H9keGPcbfaSc3jqI4+NZbpLlckaHsIhhfD0f1fWiqi2SPRZJffvkls2bNYv78+SxZsoShQ4d6DfXtT5hyz3qeddQPJ8o2bdqwZMkSTjnlFJ588kmuvvpqwEwxPfHEEyxbtow777yzimx1hTH3RvV8EqrKwIEDKyPKLlu2jE8//RSADz/8kClTprBo0SKGDx/us81AYRVFA8jMzPSqEB599FFmzJiBy+VC/M3xEAKKSos4Odf5cNttAWlzdYIxNvbx+G2H1R5TX0UxdCjFLVuSvHs3KXl5kaHsIhhfD0eNGS3XFQp83759tGnThqSkJFavXs23337b4L58ccIJJ/D6668D8Omnn7JnT83Q+QUFBVRUVHDeeedxzz338IPjXXfgwAG6dOlCaWlpg34zFRUVlTaUl19+mRNOOKHK/r59+7Jz507mz58PQGlpKStWrKCiooJNmzYxZswYHnzwQfbu3UthYWG9+68PVlE0gIyMDJ8KISMjg5ycnIha9VtacpAORaBRURCgabDVsSZ3b5+KquWBfsKsC7eBdfmLLwLwP3/jfEVF8ZHjWXOmR3EkOB9EIr4ejhozWm7Xrh2jR49m0KBB3HzzzTX2jx8/nrKyMo455hj++te/ctxxxzW4L1/ceeedfPrppwwbNoyPP/64Ms+DJ5s3b+aUU04hPT2dSZMmcd999wFwzz33MGrUKE477TT69etX776Tk5NZsWIFw4cP54svvuCOO6pmUYiLi+PNN9/klltuYciQIaSnpzNv3jzKy8u59NJLGTx4MEOHDuVPf/pTnR5gjcaX8aIpbzbMeFXOfnCoKuihju0C1mbCNfG6K8EYtDv5m3M7wLgNrEmgZaCloG0TE/02sF7shCKfFWHOB6GivmHGm2M+8pKSEi0tLVVV1Xnz5umQIUNC1ndycnLI+qpOfY3ZkR4U0BIAWuwyw9Kyjh2IC1Cbx/Qdwtp23zNqs4nSuJ3Q22PcBtbjgGhgKbC7uJhp06b5NZJb2b075fn5nAikAO7Be7idDyIV94i5OZGXl8eFF15IRUUFcXFxPP300+EWKSKxU09HADGbzXTM7J9WB8xgm350eqXnU18Iiz3GPc3ltkosqlZeF3+5/34WREURB5zqlEWC84EldPTp04cff/yRJUuWsGDBAkaOHBmyvoNtVwgkVlE0c7Kzs0nYvheAbRAwg23bxLasaWfeP3PLLWGxx7if/N02hvnVyusiIyODRCcH+ATCo+zCjfrh9WNpXjTkmltF0cyZNm0anR33963O7yMQBtu2iW35yR3wfdmyRrXVUDIzM+mWmMjpQBkmWUl9RwRDnHzC16alRYzzQahISEhg165dVlkcQagqu3btIsGftUYeWBtFMycvL48uzhTR1oqq5Y2hbWJb5nd3PnzzDZSXm4B7ISQjI4NeX35J7DPP8CmQ5HLxSH2TEA0dCm3aQE4ObNwIHou9mjvdu3cnPz/fa+gIS/MlISGB7t27113RA6somjk9UnvQucgohW0eiqKxBtu2iW3Z1Bp2tE+kY8E+WL4chgxpVJsN4ThnlevpzzxDzlVX1b+B6Gg45RR45x2YPfuIUhSxsbFVVkFbLL6wU08BIBJDi7u585476eLYzNxphgJhsG2baIYpP/ZxfM6/+qpR7TWIHTvgiy8gJgYcW0ODONUxZX/xRWDksliaGVZRNJJIDS3u5pzzzqGLs/h1G4Ez2LZJNDFx5vV0ppvcOS5CyVtvQUWFiQbbtm3d9X0xZox5/eILm/XOYvFCWBWFiDwnIjtEZLmP/SIij4nIOhFZKiK1ZKQJD57B0hKdTYuKuOfWW6GoyGxBjsNSG0WHD9LZGVFsKCoKmMHWPaKY3d05t6++CtlN1j2C+/J3vwNgXmPXPQwYAB07wtatJleHxWKpQrhHFC8A42vZfyZmPVcfYDLw7xDI5D+rVvG73Fw+wyTyLvLYVm/aBMnJZmvXDn76KSwiHtq5lbgK2J8YBYmJAWvXrSgWtNgPHTrA9u2wbl3A2veFewR3ODeXk4AS4NczZzZuBCdip58slloIdyrUr4DaUqSdA7gTHnwLtBaRLqGRriaetoj0Hj04NGIEfwHGAW0xNy23oigWMTfmmBiTHOe998Iic9nmTQAUtIoNaLuJMYnER8dTUn6I8tG/AOAvxx0XdDuNewR3PubH+zGw3VmN3Sg8p58sFksVwj2iqItuwCaPz/lOWQ1EZLKILBSRhcFw96tui7g0P5/4oiI2dOrEhXFxdMdMOyUDHZKSePull8y003PPmQbmhSfdd8WWLQDsadWwbHa+EJHKUcU3ScZOMWD37qDbadxuvSc6n9+rVt5g3COK2bON3cNisVQS6YrCW3YdrxPhqjpDVUeo6ogOHToEXBBPW0Qq8Hun/PdRUZzz3HPE+Aot/gvztM28eWExlOpWoyj2tqnfAht/cCuKexcaj6eTPPYFKwqr263XHatzWbXyBtOrF/ToAbt2GVdfi8VSSaQrinygh8fn7sCWcAji+cR6FxAPvAx8vG2bz9Di2dnZpJ16KtsBdu7kvYcfDrncUdu2A7C/TXIdNeuP2/Pp80MF7AOOoupwLxghxzMzM0lJTORo5/NPBCg+kwjr09IA+NOQIRHn5myxhJNIVxTvA5c73k/HAftUdWtdBwUD9xPrIOBy4DBwO76fZCunqvLycE86fXjbbSG/+UTtMNNwhe1SAt62e0TRLrUD3zhlJ3rsD0YU1oyMDLLuvZd4zJxk+wC5+2ZnZ/PAd98BMIbAxcSyWJoD4XaPfQUTy62viOSLyFUicp2IXOdU+QjYAKwDngZ+FyZRKxO3ZGK+tKeA7bU8yXpOVblvosMPHw55Upy47QUAFLVrFfC23Yri3EvO5btYYyx3Tz8FMwrrOX37AtBj3LiAuftOmzaNjw8fBowb3nBsEiOLxU24vZ4uUdUuqhqrqt1V9VlVfUpVn3L2q6pOUdVeqjpYVReGS9aMjAyefeIJzgJKgZndu9f6JOs57eIeUfyC0GeAi9tpnMqK2wdBUSQYRXH0kKMZfcstABxHCKKwrl5tXhuQVcwXeXl55ANPAHHAa0BLQn+9/CWSowFYmh821lM9uHjECACi+vZlkftm5YPU1FRyc02i6h+AQ8BAYFA9g3E1lsSCvQAc6tCIlcs+cI8odhfv5vSp0+Bvf2OoCDmrVgV0zUYNgqAo3NfrJmA0MBSYAdzSo0ftB4YB97Sme8TqniYDjqjot5bQYRWFw8qdK5n07qRa64xZtJsHgK/it3PT08fWWjf6umiicqKoqKjgELDoPfjFDhh9ejSb92+mW0uvXr4BJ2nXfgBKO7avo2b9cRuz31n9DlsLt3JP95Z0z9vLPY+fz4a+HQPen5tbv/mIo4EHd3/AqvcCM8hMvTGV/Hn5HCor56JCWPQlXFQOZSekBaT9QOI5renGPU1mFYUlGFhF4XDw8EEWbFlQa52xzsLj75P31lkXAI+lgfN6GUXRZW0O7/30Hr8bGQJzS1ERCQcPcSgapDGxkHxwVJujAFhdsJrVBas5qS1cmQc7vvyIF4oD3l0lf88xr//c+wlbFwew4cHmZS1wbSt4+W04662vKH3hELGxgV2H0hCys7OZNm1a5Ui1OpE6TWZp+lhF4dC/Q3++u/q7Wuv0XHk38CHnnTOVky6tX7TSNt2+hPm38ItN8M3BEMX/37bNvKRAYlxSwJs/vdfpfHDJB+x0zqdfyRewOIs/RP+CYb+8OuD9AcTvOUD76X/gcHICmRlPmvAbweCXsOfjq2hTrGzbvJbOaYOC04+fVJ9u8obN9W0JGqra7Lbhw4droMjKylKXy6Uiogvi41VBdfbs+je0dasq6P449Mb3fxcw+Wpl7lxV0Pnd0H8v+Hfw+/vuO/P99O8fvD6+/tr0MXJk8PpQc91XtkEV9OT+7TQrKyuo/dWFy+VSzGJTr5uIKKAulyvsslqaJsBC9XFPjfR1FGGletgO16FDALzdgJW72Z9/zsaEKFochu9nPBcaL5WtZsnJthQTmynoDBkCsbHG2HzgQHD6CIIhuzru6741znyO3bcr7GsqaptWEpHKdKZ2/YclGFhFUQueRsM2QAegEPi/v/+9Xu24bzzfxJgYQscUlITmz+xMPW1tAUmxgZ96qkF8PAwebEKV/PBDcPoIgaJwX/dtThzFzrHhX1Pha1opOjq6Rs7rcMtqaX5YRVELnk9xfZzXNUDepk1e6/vCfeNZ5kyn9z8Yoj+zM6LYmgKJsSEYUQCMHGleFwZpyUsIFIX7um91/h2do6qWhwP3gk9PkpKSKC8v91rfGrYtgcQqilrwfIpzxxZaQ/2Nhu4/7SrnP91/f9XyYJCdnc3rjz4KmKmneXNCFL3WWWvCAj+8whrCqlXmNYiKwn19tzmfu2jV8nCQkZHBjBkzcFULPulyubzWt4ZtSyCxiqIWPJ/i3IpiY0xMvUNTuP+0q0yECPrvqVoeaNxTXSkHDwJm6unhBx8Ozbx1MEcUJSWwcSNER0Pv3oFv38F93bc5ir1zeXBDkviLt+CTvkYa4ZbV0szwZeVuylswvJ5eNTPvOve66xrURlJSkkaDlkSbdtq3Sgiad4rbQ+YHR+bh16B0Mh4xQefwYdWEBOOZtGtXwJrNysrS07t0UQVdFxMTdM+erKwsPb9fa1XQ2V0loj2JPD3zrNeTpaFQi9dT2G/qwdgCqSgqSU83X9d33zXocPefeWk7c/POvuNPARbwZ9yuktsdRdHtTyjtjAtlSDj+ePNdffppQJpzK9rznfN5FzQpKSnoN8T1X71nFFOn2KD2Y7FEArUpCrvgzh9UYc0a875Pn9rr+iAjI4OMjAw+G9GWwbv2MKptywAKWJXU1FQ65ubSEdiRaKaeKA3hvPWIETB/vrFTnHZao5tzOwO4rRKrCU3IinY9BwLQdl9p0PrwRoVW8NTCp8jfn++zTlR5BX0WbCDh4CF2uNqzM7UdZXE//52HdBrCRYMuCoW4liMAqyj8YcsWk9a0Qwdo06ZRTW3v0RYW7UFXrQyQcDXJzMykcNIkKCsjayBURJl1FCGbt3bbKQKU/tVt9HcibPBTtfJg0bJLGqVR0KYECvcXkNIy8PGyvDFv0zymfDTF6742RXD1DzBlAbj2/VxeLvBhHzjnEirzQp6QekLIYopZmjfWmO0P7tHE0UfXXs8P9h1lAkDFrVnf6LZ8kXH++UyKN7GJXhhqyp7855OhCxh30knG4Pzhh/DSS41uLjU1lURggvP5K4/yYCLR0exsafKB79oYPMVenW2Fxt+qe1x3Yr+Khc+Bz6HVJ7D4UXhwllESu7q346cTB1CQ2gER4Zdr4Ml+/0fvtsbQv6pgVchktjRvrKLwhwAqiqJexp0xeX391mLUiw8+IP7gQTQ9nWXOA+WkSycFr79qZM+dy+2tTP6LQ5dfzifTpzeqvczMTC6IiyMF+BZYT+g8e/a2NrnG924M3U13b8leAPYs30PpF6XwNfA1/Go+pB6C9TEx8OGHtMvdQd+vVtA+dwdREyYC8DtGclKqSR+1dtfakMlsad6EO8PdeBH5SUTWichUL/snichOEVnsbMGJNFcXAVQUenQfKoDW+QXgZFQLOC+8AMDhSy8BzLSTBCt4XjXcrrmZu3fzBCa3+NC77uIdZ01HQ8jIyCBzoLEXvEwIEiN5cLCtSSFbtGlD0Ptys6/EzCkd3HWwSrnb4vBAWRlMmABRHn9f9/qVhQvp087Y0dbutorCEhjCpihEJBp4EjgTGABcIiIDvFR9TVXTne2ZkArpJoCKonWbLmxsA9EVCuvWNbq9GmzbBv/7H8TEcOC8s4EQhe9w8Ax78idgFtAR6OxkwGsQBQV0X7YMoqN5bNu2gKU/9YcSJ+FTyWbvob2Dwb5DRlG0Svg5K2E7YBxQBizwlvzKY/1Kn7ZWUVgCSzhHFMcC61R1g6oeBl4FzgmjPL4JoKJon9SeVW6b6KogTGdkZ0N5OUycSFGbZCCE4TuoamAuAy4ESoBRhw7Brl0Na/SNN6CsDMaNg06dAiGm31R07ACAbtkcsj7dU09nn3Z25WK6X2E8T2ZHRXHT/ffXPGj4cPO6aBF9Wps8IXbqyRIowqkougGeE/X5Tll1zhORpSLypoj4zEspIpNFZKGILNy5M0D5HlTh3XdhwwaT96BXr0Y32SGpA6s6OB+CoShmzjSvV1xBUal5sg/liKK6gXkPMB/nhzZnTsMada8oD0P2tqguXQGI3hGiHCL8PKIYO3psZZgO97RTylVXeR9NdeoEPXpAYSF/PM6Y/X/a8RMvZr0YIqktzZlwKgpvk+Za7fMHQJqqHoOZxZjpqzFVnaGqI1R1RIcOHXxV883BgybiqXubMwfOOAN+9SvzNHvuuQHJAx3UEcW2bbBsGbRoARMnUlxq0syFJMS4g7eQEl/HOmFYv/ii/g3m5MA335jv/txzGy1ffYnvnmZed+4NWZ9uG0Wr+FYmbMd33zEuKgpiYzn+gQd8HpfXuTMA3Tdtg/1ANFz7l2ttyHFLowmnosgHPEcI3YEtnhVUdZeqHnI+Pg0MD5o0K1ea4bt7O+UU+Owzs27iiSfg9dcD0k2H5CCOKH780bwOHQpxcWEZUXgLXnfcrbeanbNn17/BV14xr+ecYxRgiElONaPIlN1Byq/hBffUU+uE1qbgrbegosI8uNSyjufVtWaqaQTAblNWklRiQ45bGk04F9wtAPqISE9gM3Ax8BvPCiLSRVW3Oh9/CQTPRzEpydxgPTnhBLjjDmgfuIVWbRLasNppTlevRioqqnqvNAZPRQEUlzkjihDaKODnVeiVHD4MDz1klPG2beA8+dZJeTk8+6y70cAL6get08x68DZ7D9VRM3DUMGa/9pp5vfDCWo/7bO9e/gKMBNgFpAFtIW+hDTluaRxhG1GoahlwA/AJRgG8rqorRORuEfmlU+1GEVkhIkuAG4FJQRNo4MDKaafsP/+ZtN27iXriCdJGjAjo0D06Kpry5BZsTQEpLuaE1NSAtZ/73nsATHr0UdLS0vho1kdAaEcUXomLgxNPNO/rM6p47z1Yvx569oQzzwyObHXQ/iiTK7vDgXIqKrznfgg0nlNPrFsHX39tkkKdU7uvx3bHGyodiClwCtvZkOPhIDs7m7S0NKKiokhLS2v603++gkA15a0xQQHdAejwyEccyAB0WVlZKr8X/TzNBLgbH6D2s7KydJ2IKuhgR+64YXHKdPSC1y8IiOyN4v77TaDAa67xr35Fhepxx5ljHn88uLLVwd4E870WbFoTkv7a3N9GmY4WHNihetJJ5juYNKnO47KysnSt8xsYkoYyHY26LMpGkw0xwb6HBAts9Fj/8ZXEPlAhul0ul3Il+vhIoyj+CxoXgPYH9eihCloMGuOWO93cLK5454qAyN4ovv/e/Nx69fKv/ty5pn7btqqFhcGVrQ7Wd4pTBV0z552g91VRUaHRd0Ur09Gyfz5ivoNOnVQLCvw6fqOjXK9qYa5958zOwRXYUgNf95Do6OiIDgVfm6KwITyq4SvQXKAC0OXl5UER/Gsk7I6CicD7wM7cxi3oauukZ12OWb8AgONsFEqvJ58MHQqtWpmpJH/O9aGHAHisvJyoFi3COnzf39pM3e3PXRP0vg6WHqRcy+m9HQ798U8AzLnkEmjXzq/j084/H4CnLvotAAXlBZRVlNV2iCXA+LpXlJeXo6rk5uYyefLkJjUdZRVFNXzN5wZqnjc1NRWKYFVHOKUfbAfOAL6Ij4cDDfesOdXxhvnRs9BRFOG0UVTO1cbF8Zk7ZElddoo1a9D33qMEyNy3L+x/rqL2JiR88aaNQe/ruVeeQyrg2fchCcgCJsyY4f95Oyu0Y35YTI+WPSirKCNnb06wxLV4wZ97hTtMflOhTkXheCXVWdZcCHZqyczMTGIOG2ezZe3hZGCziFm5/M9/NrjdSwcZo6unoohNMpoi1F5Pbtxxn3Jzc1FVPiw2Xlgb3J5M3qiogLvuQlR5EdjhsStcf67SjuZpvnRLEAM5Otz/yP2cnAsnbTY5u2+knuc9dKhZHLp0KYNT7ArtcODtHuKNYIfJDyT+jCje8lL2ZqAFiRR8JbEPVGyhjIwMLjrbWWebDCUuF5+ceioAb91xR4OnWHrtM54y2zp1qpR7/FnjgfCNKDzjPgG4l9u1/+Ybsz5Cq62vPHgQLrgAXn6ZUuBhL22G48+lnYw7r2zbFvS+tu7eSrrTzTuYle1Qj/Nu0QJGjYKyMq6aVwLYmE+hpvo9JDo62mu9puSN5lNRiEg/ETkPaCUiv/bYJgEJIZMwDHhLYh9Ixp9obuCXXHUJmZmZPPvNNwAcDQ2bYjl0yKxREOHtdesq5e55tBn4hctGUf3mthyz1L6lKvzmNzB+PHz5JXz7rXkdPRrefhtateLqjh0rExR5Eo4/V0w343Yas7OBsarqQcfUjgx0hlErPMrrdd5OWPcJby2lTZEdUYQDz3vIzJkza4wwRITc3Nwm4zpb24iiL3AW0Bo422MbBlwTdMmaMe2TzIq7gqICpk2bxrIS8+TXBxPXpN5TLCtWmDAjRx8NKSmVxe4Fd+EaUVS/uSkm6uMtbduaFcaffgpjxsDxx5vXJUugd2/49ltOf/jhoE4B1ofE7o7CLdhXR83Gc/6l5zPQCSvlVhT1Pu/TT4exY0k4UMytc+2IItx4jjDAKAl1RtNNxrDtyx3KvQHH11Un0rbGuMeGggWbFyjT0fSn0lVEFNAtZiJGUx1XOhHxv8FnnjFulBdfXKU4460MZTo6c/HMAJ+Bf9TqT759u+p116mOGvXzNmmS6q5dVY53uVxhdyl8ZvqNqqDL2xB0Of79/b90b7z5LXSkEf0tXGjcpaPRHlcGX26LfwTb/b4x0Jh1FJgZkc+B5c7nY4Db6zounFtDFEUob0o5e3KU6Wj3h7tX/nC+dBTFuIb8cKZMMZfygQeqFP/6tV8r09E3VrwR2BOoB5Fys28oWVlZmtomQRW0ICH4i6eeePMWVdADrRIb1U5WVpa+FhOtCvr8EJToprHoqynjz2/d/WBYfavXg2GQaKyimIPJHfGjR9nyuo4L51ZfRRHqlZSFhwqV6Wj8PfH60ksvaVJSks5wFMXvGtL3L35hLuWnn1YpHp81XpmO/ven/wb4DBpGU1QaLpdLBbRUzPWJiwruE+Bzf7tQFTQnvWej2nG5XNoT9FAUWg7avXXkPLk2R/y9hzTnEcUC5/VHj7LFdR0Xzq2+iiIcFy/x3kRlOnrg0AHNysrSzNatVUGfa9GifjfQsjLV5GRzKXfurLLr5OdPVqajX2z4IsDS15+mGtbA/QS4oaVRFH1TgvsE+OZvj1cFXXreiY1qxy337C5G7rHtIufJ1RtN8SHCE3/vIZH8P2isovgY6AX84Hw+H/i4ruPCudVXUYRjONjj4R7KdHTD7g2m4N13zeUYP75+Df34ozkuNbXGrpEzRirT0W83fdt4gRtJJD9J1YZb7g97mBvur9oFV+7ZY3qqgn532xWNasct94tHGbkntY/c7zuSb57+Up97SKQqxdoUhT/rKKYA/wH6ichm4I/A9X4c12QI9mpsb3h6PgE/p1ldU88wEZ99Zl7Hjq2xK1xhxr0R7NAowcK9eGql44DVPyq43ldd8kwiibL+fRvVjlvuTY4jew8Nn9dYXVRfbwNNb+Wyt3vFMGB+XBwce+zP23HHkfGvf5HTuzcVs2eHNP97o/ClQapvQDLQwt/64dwi3Uahqnr6S6cr09GP1nxkCkpKVKOizHbokP8NnXaaGVG8/HKNXUc9epQyHV27a22ApG449RmaR9rTVlZWlv5uZLwq6Gtd4oInU3m5HoyPUgWd98P7jW4uKytLbxwVqwo6s0t8RHyX3ohkA6+/eLuHfB5lrqXP7dRTwy12FahlRFFn4iIR+b9qnwH2AYtUdXHjVVX4cWv0adOmkZeXR2pqKpmZmUHV9O4Rxdd5XxMfEw/A8d06kbhpK99+9TJFveoezUSVHOLEr+YQDXzdO47SjVVTjbrzGkRCUMDMzEwmT55c5cmx+hOuO+SHu47bxxwI61NXRkYGpfu+ggUzGJ3cmm7BkiUvj6RDFWxPhuSurkY3l5GRwe5VM+G7zzilZ29SI/TJNTU1lVwvgSJVlbS0tKD/FwOBW77bpt1GXkkeJ7Vrz6krCyhPiGdN9mNoXJyjHiqIPljM0Rk3onO/5sd1X1OR6Hv98uBOg0mICf/6Zn8y3I1wtg+czxMx2emuE5E3VPXBhnYuIuOBR4Fo4BlVvb/a/njgRUwK1F3ARaqa09D+aqNGVrYg0ym5EwD3zb2P++beB8DHcTAe+NuzV/JBv7rbGLcePjsEP3aGkz4632e95LjkQIjcKPxRxrVNQYT7RhE7cDAA7fN2mXhUgcpK6MkKs8RuRQfoFd8qIE2WdDF5d5O2BX9VeUPx9hDhJlIeFvwhIyODTa5N3Pr5rVz0XzOl/PTAQ1y/7Noadb/vAiO3lDJt+kl80sd3m+OOGsdnl30WLJH9xh9F0Q4YpqqFACJyJybW00nAIqBBikJEooEngdMw+bMXiMj7qrrSo9pVwB5V7S0iFwMPABc1pL9I46qhV7Fm15rKvNYART3XwPrNTNBeFKbVPaK46rt1wCZWD0tlTFovr3VOSD3h59zLYaYuZRzJdowO3Y9mawp0KSyHvDxISwt8J25F0RGGB+ialXU1capStu8NSHvBwPMhwtvIIlIeFvxhfv58WhXDpKUCKLPPHsiIrjVH9MuH5DNyyzbOWAqfeAQgiIqKwpXmolWbVizetpjlO5aHTvja8DUn5d4waUrjPD7HA6uc9z/WdXwt7R4PfOLx+Vbg1mp1PsFZGY5RagWA1NV2pK/M9snjj5u5S3+zwKWnm/qffRZcuUJEJHtGLdu+rDIroX74YVD6KL/sMlXQ6yai5RXlAWnzvq/+pkUxjtz79wekzWDS1O0Vg/81WP94hvN9jx3ru+Ls2aqgS3383osOFynT0YR7E0ImO430enoZ+FZE7nRGE98Ar4hIMrCy9kNrpRvgGbc53ynzWkdNju19mBFO86Q+nk87dsDixZCQACecEFSxgo07Z0Vubq7bBlZJpHjqdGvRjZUdnA+rVgWlj4rlywDY0D2JKAnM1FZKfAvyWzof8vMD0mag8JZXOhweiIFCVcndvYEbvncKbrzRd+Xjj+cgMBjoXG1XXl4eCTEJxEXHUVJWQklZSXAErge1/hrF/GtfwAQB3Iu5UV+nqner6kFVbcxYULyUaQPqmIoik0VkoYgs3LlzZyPECiN+Kors7GxuHDgQgNnl5bTv3r3JJnH3zFkB5s/mVhaBDvHeGFontGZdJzNTe3j50oC3n/3SSxxevASARbElAbuOKXEpbIpARVE9V4nbFjFhwoSICQZZXwqKCjhpxUF67QHt2RMmTvRdOT6e7xOMkfq0artSU1MRkcopY7dTSljxNdRwbxjvpoC7sGKnnmpSVqYaH1/rNIHbDe9Zx4fipmrD1qayUMntBouXoTcRMt1UnYwpXVRBi0amB7TdrKwsHZBg4kltTkG5PnDX8Y0Vb+gLQ5ypkGefDYC0gaG2acZIdJH2h+/yv9Nn053v+v7766y/4NJLVUFf9PH/Pfrxo5Xp6Oqdq4MtuqrWPvXkzw39SWBkXfXquzk3/g1ATyAOWAIMrFZnCvCU8/5i4HV/2m6yikJVdeBAc1kWLfK62/0H2+QoiiFN5CbriTef8+pbJM5Jn/vwKFXQwy1TVCsqAtauy+XSC5zr+elRKFcG7jp+vPZjvfdE5+Y1fXrjhQ0QTd0W4Y1Xl72q33Zzvus5c+o+YPlyVdAdUVEq1Izwe+zTx4Y0skJtisKfidAxwHwRWS8iS0VkmYg0euytxuZwA2bUsMpRAitE5G4R+aVT7VmgnYisA/4PmNrYfiOeOqafEnJz+Qjojsm37e1CRIKXUG14c4OtTiTOScd3d7EnAWL3F8L27QFrNy83F/dipY97A86UdCCuY3JsckROPTVlW4QvNuxeT3/3rHf//nUfMGAAdO1Kh4oKKpYsqbFK2z31tLdkb8BlrS/+uMeeGazOVfUj4KNqZXd4vC8BLghW/xGJW1E8+ih88w3Ex4M7lWJBAcuAWIzB6Hd4N9hE+p+trhtgpM5Jd23ZjVXt4Rf5mIyCnaubIRvGxR07ctyOHRREw4zh4E7tF4jrmBKX8rMxe1Pwc377iz8LMJsaezasoOVhKG6dQmKHDnUfIAKnnQYzZ8Jnn5G9bFmVdUadf29+X01CUahqLoCIdKSZp0CNCIYONa/ffmu2asSI8HxUFLeUl+PNZN8U/my+VuKCMWBH6krcbi26saqDoyhWrQIn13ljebhdO9ixg4e7wMF4oCRw1zElLoVN7rV7ETSiCEc0hGAjjjdccW8XfsdCOP10mDmTXU8+yeRt2ygqNvHZcnNz2Tx3M6Q3EUXhTAP9A+gK7ABcmKmigcEV7Qjl/PPh3XfN1EZJicmHXVFh9kVFIePGEbdyJUnTpiF5ebRt2xaA3bt3N5k/m6+nyUjxcPJF1xZdWRhoF9m5c+m8ahWHkpLIGhwD7KdlfEv+NeNfAfkuInVEAdUWYKqa3/3115v3AO3bw+23GxfwJkDyOjNSlv4D/D/onHOgUyfabdzIGcA7HrvKCsuAyFAU/hidl2DWLvzofB4DzKjruHBuTdqYfYTQFD1bvtz4pZ75G8dYOWZMYBo94wzT3h136E2f3KRMRx+Y+0Ddx/nJ/pL9yp3owVhH7n37AtZ2wFi9WnXcOPUaOO/ee8MtnV+UlZfpUyNFFfTQP/5ev4OfeEIVdCVotKdx/wSU6ejUz6YGR+hq0Ehjdqmq7gKiRCRKVWcD6cFQWpYjh4yMDHJycqioqGgyoZa7tTRTT0BgRhSLFsEnn0BKCtx4Y+WTY6sAxXkCSIpNAoH8Fk5BBE0/AfCf/8DgwTBrFrRpA/feC//+N9x1l9n/wAPQBNZF5e/Pp98OMxKKG3RM/Q6+5hryYmLoD1zmWe44NUTCiMIfRbFXRFKAr4BsEXkUKA2uWBZL5NG1RVdyW0FRLLBtm1kd3xjecSYafvtbaNeOfYfMwqpWCYFTFNFR0STGJEaknYLSUrj5ZvN61VXG02/aNLjuOrjjDjjzTDhwACLY5uZeXZ42LK1+Hk+exMWx6eqrAZiOiZEEEKdxAOw9tDcAkjYOfxTFEqAI+BPwP2A9sDqYQlkskUhSbBKJMUl85Tgjzezdu3ErqOfNM69O0im3ogh0EMcqq7MjyU4xf75RBP37wzPPGJuEJ/fdZzyD/vUv2LgxPDLWgufq8nYp0LEIDsRA9pw59W5r9BNPsKdHD1yYrHAul4s/TP4D0HRGFGNUtUJVy1R1pqo+BowMtmAWS6SRnZ1N8Y5ibj4dyoBLDxzgX1df3TBlUVpKmaMoOp5zDmlpaazPXw8EduoJqhm0I2lE8b//mdczzvC+f8gQuPRSM+L4619DJ5efeK4H6u+4Ba1KhGm3317/xqKjafP44wA80q8fOTk5/OrMXwERrihE5HoRWYZJgbrUY9uI93VelgDjLWiaJXxMmzYN3acs7wSPtTJJVB4qKeH2226rd1sfP/AAMYcOsQbYiXGH3LBlAxDYqSeo5iIbSSOKTz4xr+PH+65z990QFwfZ2bA0sm47nuuB3JNNK2MasVBywgRITobVq2HLlohacFfbiOJl4GzgPefVvQ1X1UtDINsRjbegaZdddhkiYpVGmMjLy4MD5v10F2zBBCwb24Abw7cPPwzAPI8yjTPG0EBPPSXHJUeMi6z74aezCPzwA2WxsXDSSb4PSEuDK6807994IyQy+ovngsgBh83rKm3EQsnY2J8jQX/5ZdNQFKq6T1VzVPUSVc312HaHUsAjDfcf6dJLL60R5kId/3J3pE2rLEJLampqpaI40Br+7JQ/GBUFe/fWq63+e/YAJmZ/Jc5ygWBMPUVCGA/Ph59xTtns8nKy33671mN+6+z/4cEHI+o3n5mZWRnptn+hKVuvsY1bKOlexDl7dkQpirCveQjG1lTXUfgTLM9zi/Tgf82NrKwsjR0dq0xHOctcg6+iooy//3331aut/OhoVdAB7usZbXzmuQOtCGDAQVXVc189V9v8xaxL2Ocl+Fyo8IwY+5KzTuKPtfyO3f+HFNBS0DLQzomJEbXmJisrS3sc1UNzW5rzeeeheq6hqM7335vfU69eWlFRoXH3xCnT0eLS4sAIXAs0JnpsU9yaqqKoLey2t60pR9psqvzhqT+YG/ol5gY365ZbzN+oc2fVkhL/GsnLUwXdAyru65lkFEXK3SkBl3n0P0Yrd6KFYm5mLQhPOHp3xFgB3e4oin61/I49/w9fO/XPjsAHpFUbzM29JEZMqoDGUFqq2rKl+U3l5mrHv3dUpqPbDmwLjLC1UJui8CcooCVE1NcIFunB/5ojl0y8hEeffZRhpwxj0cuLzO3r44+NofXll3+eT68Nx9up6JhjSN23j7y8PLr06sIWttCxVceAy7xkwRLoB/lx0PcQ9ABWhjgP9fxN82l9Zmv27NnD0ELouAzy4mD1MGjTtjUPz3+4xjG5XXNN4CBg1iY4IR/GdYYPuuZ6rZ8Qk8BFAy+iXVJok2AWLJoLwOYuyRzlDuDZUGJi4OST4YMPKqefdhzcwd6SvXRK6RQAaRsoVth6ttSgtmB5IlJpo4CmEfyvOdKtpcnWu+XAFlMgAjfdBJdfDg89BJMmmbLacBRF1wsuIMdxpVy4ZSEjnx4ZcPsEQOFuM4G+PtEoiiGYHMahCkdfXlHOGVlncOBYY+A54ytT/r9BwHjYwx7+/Omfax7o4TU7KxemPw/jyky51/pAzt4cHjztwcCeQB0UL10EwK60jhwViAbHjPlZUZzQGgi/ncIqigiitmB50LwibTZVOiV3QhC2F25ndcFqoiUaxg0jrUsnYleuZPPrz1I07uRa2+gx5wsSgPyBqRTvWgvAyp0m/XygPZ4AWiW2Yh/7mNUOJuw1eQNeIXQj0j0lezhw+ACJMYmcnHwyE5d+BpQzJz6B8a1Oob+PlcyrVq1i1qxZlJWV8V0FHIiGAQXQdTZsSYCYmBjGjRtH//79WV2wmo/XfczWwq0hOadFWxbx7I/PUl5RznHffwbAoT4BURNGUYBRFGP7AlZRWDyoK/SyVQzhJzY6lo7JHdl+cDv9n/z5BnfTIPj7Vvhp6jWMneT7+KTDsG8plAv0W3AFB6stDQj0GgqACeMm8MquV/iwMzy83iiKlMTEkI1IC4oKAOjesjsfT34X/tICpILsj7ZC69a+DzwDsjtkM23aNHJzc5kDnAWMnQMvAWWUsWr5Kj7O+Zi3V73Nx+s+pvBwYfBPCJj6+VRmbZgFwGVGx5OYHqB1yMccw6GUFOLz8lj3ch4cB/+d9V/O6O1jYWII8GdldsARkbYi8pmIrHVe2/ioVy4ii53t/VDLGQ6aYrC8I40/HfcnerftTafYTsTsj4Fd8ExvYX8cnJoDN3wVRaeYjvRu27vK1im2E8f+BDEKS9rDwUKQPUKn2E70btub/u37c2W6HzaOenLScWadwpbuyawH2gNv/uUvIftt7TxogiB1SO5gbDmlpdC3b+1KwsH9fxARZjllYz32u6fPUuJSADh4+GAAJffN1gNm5PJIrxs4YROUJcYzdFJgEnBmv/IKHzt5KcY4M5z/eek/YXUNDteIYirwuareLyJTnc+3eKlXrKrpIZXMYqmDW064he653Zk8dTJlRU7OAJS7gYeAx7+oYOScXcQ9fT8Xu43b5eXc3KkTf9hlPn6zE3gcFCXBlcDanLVBk9d9Ez3nwnPp1bEtPP44Z5SXB62/6rhHFO2T2sOCBaZwZP2evlNTU5nl2O/GVSuHn88xVCOKXcXmQv52pQncF3PeBdCiZW2H+M20adM4q7ycc4ExO0w+6NKo0pA6H1QnLCMK4BxgpvN+JnBumOSwWBqEt7zf/wAuxUTQvLy8nGHXXgsXXmi2wYP5+65ddAd+BB7wOC7YRuXk2GTAuYlOmGAKP/wwqH16UqkoEtvDwoWmsJ6KIjMzk42JiWwDugH9qOrQUeUcg4yqmnNSSHn9XVN42WW1HlMf8vLymO28H7Mb4yCcEDrnA2+ES1F0UtWtAM6rL5/ABBFZKCLfisi5tTUoIpOdugt3NoH49Zamja8/bTYmrMd64OjSUhN24o03YNUqNkVHkwEMBzZ7HBNso3KVp+1TToGkJPjxR9iyJaj9uvE6ohgxol5tZGRkMOPpp/nWWQl9RevWVTIiVk49lQZ/6unA4QOUVZQxZlsCUes3QJculRGAA0FqaiorMTHAuh6Go/YACeF1hw+aohCRWSKy3Mt2Tj2aSVXVEcBvgH+KSC9fFVV1hqqOUNURHfxJbG6xNILa/rRLgaHAtR06wGuvme2DD5j7zDO8m5SEetQNhZtzFUWRkPDzTe3jj4Par5udRebBrWtUK1i50qwVSE+vdzsZGRmc+/rrAExNSSHjwgsr94Vy6smt+H67LMYtGDR2/YQH7tAgc53PJ+ZCdHJ0WN3hg6YoVHWcqg7ysr0HbBeRLgDOq9cMMKq6xXndAHyJ+f9ZLGHHM86PN8qTkjjpkUd+nno66ywumTSJGTNm4HK5EBFcLldI8oRXf9r+3sn78PbVV4ckwKT7xtonr9Dkfx80CBITG9bYmWdCv34mZpVHkMDkuNBNPWW9nUVcGUxYZPr60MlbHygyMjKYMWMGy9sYH5+TcmHA8AHhdWzxtWQ7mBvwd2Cq834q8KCXOm2AeOd9e2AtMMCf9ptqCA9L08Iz73e7du20Xbt2EZkDfMPuDcp0NO2faZqVlaVHJySogu4HjQ1BOI8zs85UpqPLb73KhKa4+urGNThjhmln6FBVJy5WeUW5Ca0yHS2vKA+A1N7JysrS+IHx+qsLTUiRH4P5/S1YoAq6pi066ulRgW+/GjQyZ3YwuB84TUTWAqc5nxGRESLyjFOnP7BQRJYAs4H7VXVlWKS1WLzg6cpcUFBAQUFBDbfmSMgp4vm0PW3aNNaUlLAMaAGcABQ54TyChXtE0WmVE+K8nobsGlx2GXTsaOwsX34JQJREVRq0i0qLajm4cUybNo1D0Ye4aIX5/BJB/P7S0ylPTqLPbojbXhD49utBWBSFqu5S1bGq2sd53e2UL1TVq53381R1sKoOcV6fDYesFktD8ZZTJBzh4T3n791G+K+dfUOc12B61LgVRctla0xBPQ3ZNUhIgClTAPh84sRKJRxdYewEwZx+ysvLgyTo59y3v/QsDzQxMZQdZ5Rq/1VHoKKwWI4EvLnQBvvp3RuJMYkIQklZCT1cPYCfk973c16D6VGzs2gnrYohbn0OxMfD4MGNbvPNDh0oBsYWF9PXUcIHCkwsqWAuuktNTYUk6L7ffN7kWR4Eok424TzS1x0ISvt+yxHW3i2WZoyvp8xQ+8OLSOWo4va7bycpKalSUfQluJ5XJWUlFB4u5Nhtzq0mPd1kcmskNz3wAC86790rGPSw8ScL5ogiMzOTFknRtCuGQwIFBPf7i3UUxeiNFZSUlQSlD3+wisJiCRK+njLD4Q/vVhQTz53IjBkzONjNRMEdGBUVVM+rXUVmBfOIjeYmPnPlyoBMveXl5VWG9DjGXeikIw2mosjIyOD0AQMB2BwDqcH2XDv2WA5Fw6AdsG/LxuD04QdWUVgsAcZtwM7NzUWqhRwPV3h4T4N2RkYG3+TlQWIiHSoqyHCv1g4CM980ARiG5RtF8cWBAwGx06SmprLMeT/IXegoimAvuuvhXNM26YODH48tIYFlrkSigNKvvwxeP3VgFYXFEkA8Ddhg3M/dyiJU6ya8UWNBWlSUCcwH8NNPQev30acfBWDkdvN5AYGx02RmZrIlMZFDQBrGgyu6PPjGbIDEbU7Aru7dgtqPm+X9zHqKqLnf1FEzeFhFYbEEEG8GbFXF5XKFNRqw15XL/RxTdhAVxY7CHbj2gOsg7APcPTXWTpORkcG/n36a9Y69Y2znzhw3/Dgg+IoiZcc+AKJ7uILaj5u1A7sAkDR/YUj684ZVFBZLAIkUA3Z1vIbhdiuK1au9HBEY2nRvwxnrzftZQIVTHgg7TUZGBgOcMB7v3H03g4823lTB9HpSVdrsMu0npPUOWj+ebB7sojTKcS8uCI+brFUUFksAiSQDtie1jiiCqChOOfMUzlhn3n/ilAXUTjPIsVAsXx6SMB6Fhwvpus+ouzhXgDLa1UFCmw580ROiKtSkSA0DVlFYLAHEWwyoSMhv7jUMt9tGEURF0T2tE2MdZ51PCYKdxr0mY/nykESQLSgqqFxDQffuQevHk83rN/O2k0xx1pQpYVndb1OhWiwBpK50tuHC64ji6KPN6/r1JutcANY3VKftkjW0OgT70rqQszEIYc09RhQpcWcCwR1R7CreRY8QKors7Gw+ee8T2g6Df/8XTiwuJvWaa4DQpka2IwqLJcBEYjpbr4oiKQlcLqMkNgbHR7/3AmOg2HfyqKC0T2oqpKTAjh20P2CmhIKqKPZsodNBKI8S6NQpaP24mTZtGqUHStneAua1gXjg5OLikK/ut4rCYjkC8DktE2Q7RfpSk0Gg9LTAJfapgkjlqKLrpj1AcKeeinKNwWVvm4SA5qDwRV5eHjgLst820Vf4NaF3jrCKwmI5AvCZ2CeYimLnTgbkFVMSDfGnnh749t04dopOG02CpGCOKMrycgDY3yEw+bHrIjU1tVJRvOPYzicCfXr0CEn/bqyisFiOAHzmlA7iWgr99FOiFL52Qbv2QbyxOSOKthu2AsFVFJpvwgAWdWoXtD48yczMJF7jAchpb/KttwCeuuCCkPTvxioKi+UIwOeIIoieT6UffwjA7KPjSIxtYEY7f3AURau1ZjommOsoYrZsA6C0S8eg9eFJRkYG90y7x3xIgs/btwDg5N27QtK/G6soLJYjgDqnnlatAlUCRnk5UbNM2L5Fg4P89O0oiuSfNoIGd0QRv93coLVbaMJ3AFx2gRMfty28cIEJN77v9ZeoOBS6aLJhURQicoGIrBCRChHxmcVERMaLyE8isk5EpoZSRoulOeHTmN25M7RsCXv2NHrVr2c2v7+3b0/M9p1saA27enVpVLt10rEjdOhAdOFBeuwLrqJosWMvANGpoQnfAdApuRPn9T+PLild2H1UZ5Z3hDYHy9mW9VTIZAjXiGI5xnj/la8KIhINPAmcCQwALhGRAaERz2JpXvgcUYgExKDtGQxxuCp/3LsXgGvPhvbJHRrcrt84Bu1BO4Lr9dS6wAnf4QpN+A4w+UTevPBNtvx5C1tu2so3E825Rj01I2QyhCsV6ipVrct6diywTlU3qOph4FXgnOBLZ7E0P2oNb+FWFIsWNbh9dzDEZCAbiAUeaQ+zesHcT+cGfzWxM/00aEdwRxQd9pjpnuSj+gatj9rIzs7m3m25FMZC54Wr+ODBB0PSbyTbKLrxc6ZBgHynzCsiMllEForIwp07dwZdOIulKeFrRJGdnc3vP/oIgKW33NLgG7rbr/9h4GhgKXBrL7Pv4I6DQc8V/q0TsXfEFigqLeKlrJcC3oeWltJxfwUVQJujQj+54R615a/dT7aTrWnTtGkhCekRNEUhIrNEZLmXzd9RgXgp82ltU9UZqjpCVUd06BCCoa7F0oTwpijcN56nCwrYAxxz+DCPXX11g248qamppAGTgUNABnAoxdlZFNxc4dnZ2VyXlQXAyTmAwrU3XBvQG2h2djaj+riIVtieAu+8+2HA2vaXyhD2W+DfjmU3o6yMe2+9Neh9B01RqOo4VR3kZXvPzybyAU/n6+5AEILFWCzNH29hxt03nkPAy07ZxSUlDbqhZ2ZmcnxcHGDCiS8HcMdGdNJzBGs18bRp01hSUsI2oFMR9C2A4vLAhblwK9SoArNOIz+ZoI+QvFH5/RXBkgSY3x1aASds2lTrcYEgkqeeFgB9RKSniMQBFwPvh1kmi6VJEhcdR2xULKUVpRwuNzlDPW/czzuvlwJbnex89SEjI4OpEycCVKYora4oghVq3X0ec5zPJ+cCcYFTTG6F2t2JmZifGNwRki+qfH8eo4rbY2Jg2jR4+GF44QXYvTvgfYfLPfZXIpIPHA98KCKfOOVdReQjAFUtA27AhLFfBbyuqivCIa/F0hyobtD2vPEswtzgOwCTGjh1e4yT8vXE6683odY9FEUwQ627z6NSUeQAsYFTTG6F090J7ZQfX7U8VFQJYb8FXh8IO+LAVVYGf/sb/PnPcOWVsHVrwPsOl9fTO6raXVXjVbWTqp7hlG9R1Qke9T5S1aNVtZeqhjegv8XSxKlup6ieO8M9qvhrQ8NnLzNjidHXXceMGTOIaWWyGHRp1SWoucLd5+E5oohvERcwxeRWON0dq2l+bNXyUJGRkcGMGTNwuVywFQ7FwmVTesCTT8Ldd8Mf/wiXXWbWxgQaVW122/Dhw9VisVSl3xP9lOnoyh0rK8uysrLU5XKpiOiw7t21PDpaNTpadevW+jVeWKgqohoTo3rokKqqtnugnTId3V64PZCn4ZWsrCx1pabqjnhUQR+455qAtp2UlKSvtDVt/2YImpSUpFlZWQHro77sKd6jTEfj74nXw2WHA9ImsFB93FNt4iKL5QjBPaJ4csGTdGvheJqnwrUvXVtZZ+20LPp+vZLP/nY1Cy8Y7XfbXVblM0mVnT3a8sx3/wBgd7GZK2+b2DZAZ+CbjIwMMjIymD+qGx2+38I4DdytLSMjAyoqGHTDJKCCvSnJzJjxn7DmGWmd0Jo+bfuwdvdalu9YzsovVgY1WZZVFBbLEUKHJGN7eHLBkz7rrGlrpqD2zfqQ29r57wJ65Q8wCZiVvIPbvritsrxLShdiokJ3m1k7uCvHf7+FVt8tCWi7Gfn5sL+C3Qlw9vTbyRgX/mRUI7qOYO3utTz5zpO8cvMrxnUWyM3NZfLkyUDgsuBZRWGxHCE8dPpDDOsyjPKKcp91OvTYBe89zWk7Upj6iykmxIcfTFw6C1hI25EnMXX0LyrLx/ce31ix60XekJ7AQjoudIIc+il/rcyeDbffDsBlv4aMTqG1TfhiRNcRvLL8FV6b+1qlknDj9sqyisJisdSLAR0GcO+p99ZeqaICbn6LVrt2c9/R15tUqf5w/wIAzvjVTZwx7uxGStpw9vdJZVcitNu+B3JyoGfPRrW3dtkcup13DkkVFTx1Rjs+OnoXf0hqHxhhG8mIrsY/trBlYdU7eTmggfXKiuR1FBaLJdRERcFxx5n38+f7f5zj8eQOzhcukhJS+Nr9wD9nTq11/WHLxRNJ2nOAz3vClFEmxHiXlCBHw/WTtV+tNbEqugC3e2xOVPdAemVZRWGxWKpy/PHm1V9FsWOH2Vq08H8EEiRS4lKYk+Z8+MpncGq/KC3cz+jVBykXmHX3JK4fNYVHzniEQR0HNVrOxpKdnc2N190IS4CyapsGft2KnXqyWCxV+YVjY5g3z7/67tHEoEGBsQk0gpS4FL51LwP58cdGtbXzm8/oWgEru8Rw36XP131ACKmM+/QuZvPA5XKR+WhgvZ7siMJisVTl2GPNFNTixVBcXGvV7Oxs7nbyN7+8bFnI4x9VJzk2meXuLKUrV0JpaYPbOjhvNgDre7UJgGSBxZf9QUTIyckJuOuuVRQWi6UqKSnG1lBWBgsX+qzmDpbXbc8eAOYVFoYlWJ4nKXEpFMbDhhYChw9zusvVYHmiF/4AwPYB4Z1O84Yv+0OwVotbRWGxWGritlPUMv3knv5wm6+XEZ5geZ58N/c7ABa3NhkJOmzd2mDlFb/QTKn9a/FC0tLSwj5a8qR6+BUIbjwtqygsFktN3HaKWgzaeXl5CDDQ+bzMozxcvPD0CwAsdTxYj6Fhyuv1GTPotqOQkmhYVvbzIrZIURaecZ9EBJfLFdR4WlZRWCyWmnh6Pqn3fGGpqamcCiQDm4E9HuXhYnvedgCWOHaKIU55fZXXe3/9KwCLO0PZAVMW7tFSdTIyMsjJyaGioiIodglPrKKwWCyAsTmkpaURFRVF2tixlLRoYdxeN2yoWVmV1085hY+cjx84r8Gc/vCHrh26ArDUCaDqZAytt/Jy7dgBwPfdgH0/l4dztBROrHusxWKpNExXxgvKy+Pz6GgmAtx6K/TpU/WAlSs59t13AXiuRQtuPHDAuGUGOBhdfZl20zSm5ExhYwc4AHQFUhMT6628TkpMhOJiFnQDFv9cHs7RUjixisJisfzsl+/B7PJyoyjeeMP7QUlJ8Mwz/PaSS/ht0CX0jysuuYIp902BeGEZyi+AZ//wB8bVU3mdkBBvFEU7wCQEDPtoKZyERVGIyAXAdKA/cKyqevXBE5EczINBOVCmqiNCJaPFciThbUrl30Ap8Oi9XuJDRUfDr34FffsGXbb6kBibCIDGKsdNngwzZjCuvhn7tm4lZc9e9sXDxoQYRMqDErq7KRGuEcVy4NfAf/yoO0ZVC4Isj8VyRJOamkputVzZRcB7LhePRpABty6iJIrk2GQOlh7k8KB+JAAsXVq/RhaYAIcLu8L40RN47/H3Ai5nUyNcqVBXqepP4ejbYrHUpD5++VWM3hG2vgB+TtB0sF8vU7CknrkpHEWxoCu4WkXeYrtwEOleTwp8KiKLRGRybRVFZLKILBSRhTt37gyReBZL88Bfv3y30Ts3NxdVjbj1BQDJcckA7O3TwxTUN5TH998DsKCbVRRugqYoRGSWiCz3sp1Tj2ZGq+ow4Exgioic5Kuiqs5Q1RGqOqJDfeckLRZLDb98oMbIwZvRO9LWF7hHFIUJUSYfxeHD8JOfExiHDv2sKLqCq7VVFBBEG4WqjgtAG1uc1x0i8g5wLNC42MEWi6VOarjLOiOH6krCTSStL6hUFIcL4ZhjYONGY6cY5Ed48Lffhr17WdMtgU2tSuyIwiFip55EJFlEWrjfA6djjOAWiyXI+Bo5REdHe60fSesLkmPN1FPh4UIY4qzN9teg/dRTAPxnpIBAaqvIOa9wEhZFISK/EpF84HjgQxH5xCnvKiLuxZ6dgLkisgT4HvhQVf8XDnktliMNXyOE8vLykAajawiVxuzSg2ZEAfDpp1DuO1c4ACtWwFdfoSkpzOhfTEJMAh2TO9Z+zBFCuLye3lHV7qoar6qdVPUMp3yLqk5w3m9Q1SHONlBVI+eXaLE0c3yNENxG7lAFo2sIVaaeTjsNunY1SYwefrj2A/9jvPVfiq+gMB7KdpXx8ssvB1vcJkHETj1ZLJbwUZu7bCiD0TWEKlNPLVvCM8+YHbffbkYN3jh4kMNOvX8kmym3soKyiPPoChdWUVgslhqEOox1IKmcejp80BSceSZcc43xfrriCu+usq+8QlxxMfOApZ2csr2R59EVLqyisFgsXvEcOWRmZjJt2rSIXWTniXsdReHhwp8L//EPcLlg0SIYPdooD8/NCSv+FEAr5xgnamwkeXSFCxsU0GKx1IovV1kgIkcYnjYK99qPvLw8LurYkWwRopyV19XZER3NG+Xl0NopcBRFJHl0hQurKCwWS63UtsgukhXF4pWLeer2pyplf3X7dtYnJPDA73/PmDFjahw3d+1aom69FVo557o38jy6woVVFBaLpVZ8Tb1E6pSM25g9e9ds9Jqq2fkWUMLpFY/QY82b3o+9LZmiw0ZRdEvpxgOZD0SkMgw1VlFYLJZa8RZZ1l0eiQzpPISYqBjKYsugTc39ZZSxce9G3w1EQc/WPVmzdA0xUfYWCVZRWCyWOsjMzKwRviOSp2SO6XQMO27awaCRg9iyZUuN/V27duXrr7+utY2uLbpaJeGB9XqyWCy10hRdZdsktuHBWx8k6VAS7KFySzqUxIO3PshRbY6q3OZ/NJ9Th55K73a9OXXoqcz/aD4JMQnhPoXIQlWb3TZ8+HC1WCyWrKwsdblcKiLqcrk0Kyurxv6kpCTFpDRQQJOSkmrUOxIAFqqPe6qY/c2LESNG6MKFXrOrWiwWSyVpaWle7S8ul6sy1PqRgogsUh/ppu3Uk8ViOWJpah5d4cIqCovFckTgLYWrL8+tSPXoChfWrG+xWJo93laXX3bZZWb+XQTPKfhI9ugKF3ZEYbFYmj3eVpe7lYNbWQBNwqMrHIRlRCEifwfOBg4D64ErVXWvl3rjgUeBaOAZVb0/lHJaLJbmQV02B1U9Ig3Y/hKuEcVnwCBVPQZYA9xavYKIRANPAmcCA4BLRGRASKW0WCzNAn9sDtaA7ZtwZbj7VFXLnI/fAt29VDsWWKcm091h4FXgnFDJaLFYmg/eEjFVxxqwfRMJNorfAh97Ke8GbPL4nO+UeUVEJovIQhFZuHPnzgCLaLFYmjKeq8uBSpuEG2vArp2gKQoRmSUiy71s53jUmQaUAd6yoIiXMp+rA1V1hqqOUNURHTp0aPwJWCyWZoU7EZOq8tJLLzWpkCThJmjGbFUdV9t+EbkCOAsYq96Xh+cDPTw+dwdqRviyWCyWepKRkWEVQz0Iy9ST4810C/BLVS3yUW0B0EdEeopIHHAx8H6oZLRYLBaLIVw2iieAFsBnIrJYRJ4CEJGuIvIRgGPsvgH4BFgFvK6qK8Ikr8VisRyxhGUdhar29lG+BZjg8fkj4KNQyWWxWCyWmkSC15PFYrFYIhirKCwWi8VSK80yH4WI7ARqBpn3j/ZAQQDFCSfN5Vyay3mAPZdIpLmcBzTuXFyq6nVtQbNUFI1BRBb6St7R1Ggu59JczgPsuUQizeU8IHjnYqeeLBaLxVIrVlFYLBaLpVasoqjJjHALEECay7k0l/MAey6RSHM5DwjSuVgbhcVisVhqxY4oLBaLxVIrVlFYLBaLpVasonAQkfEi8pOIrBORqeGWp6GIyHMiskNElodblsYiIj1EZLaIrBKRFSLyh3DL1FBEJEFEvheRJc653BVumRqDiESLyI8i8t9wy9IYRCRHRJY5MecWhluexiAirUXkTRFZ7fxnjg9Y29ZGUZl2dQ1wGia8+QLgElVdGVbBGoCInAQUAi+q6qBwy9MYRKQL0EVVfxCRFsAi4Nwmel0ESFbVQhGJBeYCf1DVb8MsWoMQkf8DRgAtVfWscMvTUEQkBxihqk1+wZ2IzAS+VtVnnIjbSaq6NxBt2xGFodmkXVXVr4Dd4ZYjEKjqVlX9wXl/ABNF2GeWw0hGDYXOx1hna5JPaSLSHZgIPBNuWSwGEWkJnAQ8C6CqhwOlJMAqCjf1SrtqCT0ikgYMBb4LsygNxpmuWQzsAD5T1aZ6Lv8E/gJUhFmOQKDApyKySEQmh1uYRnAUsBN43pkSfEZEkgPVuFUUhnqlXbWEFhFJAd4C/qiq+8MtT0NR1XJVTcdkazxWRJrc1KCInAXsUNVF4ZYlQIxW1WHAmcAUZ+q2KRIDDAP+rapDgYNAwGytVlEYbNrVCMWZz38LyFbVt8MtTyBwpgS+BMaHV5IGMRr4pTO3/ypwqohkhVekhuPkwEFVdwDvYKahmyL5QL7HKPVNjOIICFZRGGza1QjEMQA/C6xS1YfDLU9jEJEOItLaeZ8IjANWh1WoBqCqt6pqd1VNw/xPvlDVS8MsVoMQkWTHSQJnmuZ0oEl6C6rqNmCTiPR1isYCAXP6CEuGu0hDVctExJ12NRp4rqmmXRWRV4BTgPYikg/cqarPhleqBjMauAxY5sztA9zmZD5sanQBZjoedlGY1L5N2rW0GdAJeMc8jxADvKyq/wuvSI3i90C287C7AbgyUA1b91iLxWKx1IqderJYLBZLrVhFYbFYLJZasYrCYrFYLLViFYXFYrFYasUqCovFYrHUilUUFkuAEZHpInJTLfvPFZEBoZTJYmkMVlFYLKHnXMAqCkuTwa6jsFgCgIhMAy7HBJfciQmJvg+YDMQB6zCLB9OB/zr79gHnAadWr6eqRaE9A4vFN1ZRWCyNRESGAy8AozArfH8AngKeV9VdTp17ge2q+riIvAD8V1XfdPa181Yv5CdisfjAhvCwWBrPicA77lGAiLjjhA1ybvytgRRMiBhv+FvPYgkL1kZhsQQGb0PzF4AbVHUwcBeQ4ONYf+tZLGHBKgqLpfF8BfxKRBKdaKRnO+UtgK1OqPQMj/oHnH3UUc9iiQisorBYGomTrvU1YDEmd8bXzq6/YjLyfUbVkOKvAjc7mch61VLPYokIrDHbYrFYLLViRxQWi8ViqRWrKCwWi8VSK1ZRWCwWi6VWrKKwWCwWS61YRWGxWCyWWrGKwmKxWCy1YhWFxWKxWGrl/wEzL/GO3y3oEwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(__doc__)\n",
    "\n",
    "# Author: Noel Dawe <noel.dawe@gmail.com>\n",
    "#\n",
    "# License: BSD 3 clause\n",
    "\n",
    "# importing necessary libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "\n",
    "# Create the dataset\n",
    "rng = np.random.RandomState(1)\n",
    "X = np.linspace(0, 6, 100)[:, np.newaxis]\n",
    "y = np.sin(X).ravel() + np.sin(6 * X).ravel() + rng.normal(0, 0.1, X.shape[0])\n",
    "\n",
    "# Fit regression model\n",
    "regr_1 = DecisionTreeRegressor(max_depth=4)\n",
    "\n",
    "regr_2 = AdaBoostRegressor(DecisionTreeRegressor(max_depth=4),\n",
    "                          n_estimators=300, random_state=rng)\n",
    "\n",
    "regr_1.fit(X, y)\n",
    "regr_2.fit(X, y)\n",
    "\n",
    "# Predict\n",
    "y_1 = regr_1.predict(X)\n",
    "y_2 = regr_2.predict(X)\n",
    "\n",
    "# Plot the results\n",
    "plt.figure()\n",
    "plt.scatter(X, y, c=\"k\", label=\"training samples\")\n",
    "plt.plot(X, y_1, c=\"g\", label=\"n_estimators=1\", linewidth=2)\n",
    "plt.plot(X, y_2, c=\"r\", label=\"n_estimators=300\", linewidth=2)\n",
    "plt.xlabel(\"data\")\n",
    "plt.ylabel(\"target\")\n",
    "plt.title(\"Boosted Decision Tree Regression\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "再查看两种模型的决定系数："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8569240975984329"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr_1.score(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9752198743918248"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr_2.score(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "很明显AdaBoostRegressor模型效果更好。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AdaBoost的优缺点\n",
    "优点：\n",
    "1. AdaBoost作为分类器时，分类精度很高\n",
    "2. AdaBoost支持多种基学习器，使用很灵活\n",
    "3. 作为简单的二元分类器时，构造简单，结果可理解\n",
    "4. 不容易发生过拟合\n",
    "\n",
    "缺点：\n",
    "1. 异常样本敏感，异常样本在迭代中可能会获得较高的权重，影响最终的强学习器的预测准确性"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
